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Optimization Methods in Management Science

  • Teacher(s): R.Oeuvray (AR)
  • Course given in: English
  • ECTS Credits: 6 credits
  • Schedule: Autumn Semester 2019-2020, 2.0h. course + 2.0h exercices (weekly average)
  •  séances
  • Related programmes:
    Master of Science (MSc) in Finance, Orientation Asset and Risk Management

    Master of Science (MSc) in Finance : Financial Entrepreneurship and Data Science

    Master of Science (MSc) in Finance, Orientation Corporate Finance

    Master of Science (MSc) in Management, Orientation Business Analytics

    Maîtrise universitaire ès Sciences en management, Orientation Behaviour, Economics and Evolution

    Master of Science (MSc) in Management, Orientation Marketing

    Master of Science (MSc) in Management, Orientation Strategy, Organization and Leadership

 

Objectives

This course introduces students to the theory, algorithms, and applications of optimization. Applications to logistics, manufacturing, transportation, resource allocation, modern portfolio theory and machine learning with a focus on SVM and Kernel Machine. Exercises and theory are equally important for the success of the class. Some examples are provided in Python.

By the end of this course, the students should be able to :

1. understand the theory of optimization methods and algorithms developed for solving various types of optimization problems,

2. be able to apply these methods and algorithms to problems encountered in management science.

Contents

1. Linear programmig

2. Graph theory and networks

3. The shortest path problems

4. The transportation problem

5. Combinarial optimization and the Branch and Bound

6. Dynamic programming and the knapsack problem

7. Non-linear optimization and optimality conditions

8. Conjugate gradient method

9. Quasi-Newton methods

10. Numerical optimization in Python with SciPy

11. Portfolio Optimization

12. SVM and Kernel Machine

References

- Luenberger, D. G., Ye, Y., Linear and Nonlinear Programming, Fourth Edition, Springer, 2016.

- Bierlaire, M., Optimization : Principles and Algorithms, PPUR, 2015.

- Nocedal, J.; Wright, S. J., Numerical Optimization, Second Edition, Springer, 2006.

- Bertsekas, D. P., Dynamic Programming and Optimal Control, Fourth Edition, Springer, 2017.

Pre-requisites

- Linear algebra

- Some basic concepts in multivariate analysis (gradient and hessian of a function)

Evaluation

First attempt

Exam:
Written 2h00 hours
Documentation:
Not allowed
Calculator:
Allowed with restrictions
Evaluation:

[ Calculatrice 4 opérations selon directive HEC ]

Retake

Exam:
Written 2h00 hours
Documentation:
Not allowed
Calculator:
Allowed with restrictions
Evaluation:

[ Calculatrice 4 opérations selon directive HEC ]



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